Color appearance models (“CAMs”) have been developed to match colors under different environment conditions that otherwise might be perceived to be different, according to the human visual system (“HVS”). In particular, a color captured (e.g., in an image) under one set of conditions may be perceived as a different color by an observer viewing that color in another set of conditions. The following are examples of factors that can contribute to perceptible color mismatches: the different chromacities and/or luminance levels of different illuminants, different types of devices used to display the color, the relative luminance of the background, different conditions of the surrounding environment, as well as other factors. Conventional color appearance models try to compensate for these factors by adjusting an image viewed with a destination set of conditions so that it appears to be the same color at which it was captured with a source set of conditions. Thus, color appearance models can be used to convert a patch of color seen in one environment (e.g., the source environment) to an equivalent patch of color as it would be observed in a different environment (e.g., the target environment).
In some approaches, a traditional color appearance model usually consists of the following three stages: chromatic adaptation, non-linear response compression, and appearance predictor generation, the appearance predictors usually describe the human visual response to a colored test patch with respect to a specific light source. While functional, some of these approaches to match colors under different conditions have drawbacks. In at least one approach, chromatic adaptation and non-linear response compression usually are performed sequentially in separate color spaces. In particular, chromatic adaptation typically is performed in a sharpened color space, whereas the non-linear response compression generally is performed in a cone space. Transforming between the sharpened color space and the cone space generally requires computational resources. Further, photoreceptors do not appear, at least in some cases, to physiologically implement spectrally sharpened cone responses. In some approaches, performing chromatic adaptation in a sharpened color space might affect generation of appearance predictors, such as a hue predictor. In at least one approach, the use of separate color spaces for implementing chromatic adaptation and non-linear response compression typically applies similar computations to the different color channels, which might introduce channel interdependencies that generally are not present among the long, medium, and short cones in photoreceptors. In some conventional color appearance models, some appearance predictors are used to determine others.
In view of the foregoing, it would be desirable to provide systems, computer-readable media, methods, integrated circuits, and apparatuses to facilitate the prediction of the appearance of color in images for different viewing environments, including high dynamic range images.